A Kalman filter solves the general problem of estimating true values of variables/states of a linear dynamic system that is perturbed by white noise. It is a key method for improving the accuracy for complex measurement and control systems. Unlike filter designs that are optimized for specific signal and noise frequency spectral characteristics, the Kalman filter constantly adapts to noise in the system measurements and its changes from moment to moment.
The Kalman filter is also known as a linear quadratic estimator (LQE). It is an algorithm that uses a series of input measurements acquired over time. The measurements contain noise (random variations in the measurements) and other errors, and the filter generates estimates of unknown system variables/states that tend to be more precise than those that would be based on a single measurement alone.
The Kalman Filter also solves what are called inversion problems, in which a tentative solution is improved incrementally by comparing sensor inputs with estimates of the input that would be expected from the tentative solution and input data.
The Kalman filter has numerous applications in technology. A common application is for guidance, navigation and control of vehicles, particularly aircraft and spacecraft.